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  <h1>Source code for openspeech.data.audio.augment</h1><div class="highlight"><pre>
<span></span><span class="c1"># MIT License</span>
<span class="c1">#</span>
<span class="c1"># Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho</span>
<span class="c1">#</span>
<span class="c1"># Permission is hereby granted, free of charge, to any person obtaining a copy</span>
<span class="c1"># of this software and associated documentation files (the &quot;Software&quot;), to deal</span>
<span class="c1"># in the Software without restriction, including without limitation the rights</span>
<span class="c1"># to use, copy, modify, merge, publish, distribute, sublicense, and/or sell</span>
<span class="c1"># copies of the Software, and to permit persons to whom the Software is</span>
<span class="c1"># furnished to do so, subject to the following conditions:</span>
<span class="c1">#</span>
<span class="c1"># The above copyright notice and this permission notice shall be included in all</span>
<span class="c1"># copies or substantial portions of the Software.</span>
<span class="c1">#</span>
<span class="c1"># THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span>
<span class="c1"># IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span>
<span class="c1"># FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span>
<span class="c1"># AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span>
<span class="c1"># LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span>
<span class="c1"># OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span>
<span class="c1"># SOFTWARE.</span>

<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">librosa</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>

<span class="kn">from</span> <span class="nn">..audio.load</span> <span class="kn">import</span> <span class="n">load_audio</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<div class="viewcode-block" id="SpecAugment"><a class="viewcode-back" href="../../../../modules/Data Augment.html#openspeech.data.audio.augment.SpecAugment">[docs]</a><span class="k">class</span> <span class="nc">SpecAugment</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Provides Spec Augment. A simple data augmentation method for speech recognition.</span>
<span class="sd">    This concept proposed in https://arxiv.org/abs/1904.08779</span>

<span class="sd">    Args:</span>
<span class="sd">        freq_mask_para (int): maximum frequency masking length</span>
<span class="sd">        time_mask_num (int): how many times to apply time masking</span>
<span class="sd">        freq_mask_num (int): how many times to apply frequency masking</span>

<span class="sd">    Inputs: feature_vector</span>
<span class="sd">        - **feature_vector** (torch.FloatTensor): feature vector from audio file.</span>

<span class="sd">    Returns: feature_vector:</span>
<span class="sd">        - **feature_vector**: masked feature vector.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">freq_mask_para</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">18</span><span class="p">,</span> <span class="n">time_mask_num</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">freq_mask_num</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">freq_mask_para</span> <span class="o">=</span> <span class="n">freq_mask_para</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">time_mask_num</span> <span class="o">=</span> <span class="n">time_mask_num</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">freq_mask_num</span> <span class="o">=</span> <span class="n">freq_mask_num</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot; Provides SpecAugmentation for audio &quot;&quot;&quot;</span>
        <span class="n">time_axis_length</span> <span class="o">=</span> <span class="n">feature</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">freq_axis_length</span> <span class="o">=</span> <span class="n">feature</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">time_mask_para</span> <span class="o">=</span> <span class="n">time_axis_length</span> <span class="o">/</span> <span class="mi">20</span>      <span class="c1"># Refer to &quot;Specaugment on large scale dataset&quot; paper</span>

        <span class="c1"># time mask</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_mask_num</span><span class="p">):</span>
            <span class="n">t</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">time_mask_para</span><span class="p">))</span>
            <span class="n">t0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">time_axis_length</span> <span class="o">-</span> <span class="n">t</span><span class="p">)</span>
            <span class="n">feature</span><span class="p">[</span><span class="n">t0</span><span class="p">:</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># freq mask</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">freq_mask_num</span><span class="p">):</span>
            <span class="n">f</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">freq_mask_para</span><span class="p">))</span>
            <span class="n">f0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">freq_axis_length</span> <span class="o">-</span> <span class="n">f</span><span class="p">)</span>
            <span class="n">feature</span><span class="p">[:,</span> <span class="n">f0</span><span class="p">:</span> <span class="n">f0</span> <span class="o">+</span> <span class="n">f</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="k">return</span> <span class="n">feature</span></div>


<div class="viewcode-block" id="NoiseInjector"><a class="viewcode-back" href="../../../../modules/Data Augment.html#openspeech.data.audio.augment.NoiseInjector">[docs]</a><span class="k">class</span> <span class="nc">NoiseInjector</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Provides noise injection for noise augmentation.</span>

<span class="sd">    The noise augmentation process is as follows:</span>
<span class="sd">        1: Randomly sample audios by `noise_size` from dataset</span>
<span class="sd">        2: Extract noise from `audio_paths`</span>
<span class="sd">        3: Add noise to sound</span>

<span class="sd">    Args:</span>
<span class="sd">        noise_dataset_dir (str): path of noise dataset</span>
<span class="sd">        sample_rate (int): sampling rate</span>
<span class="sd">        noise_level (float): level of noise</span>

<span class="sd">    Inputs: signal</span>
<span class="sd">        - **signal**: signal from audio file</span>

<span class="sd">    Returns: signal</span>
<span class="sd">        - **signal**: noise added signal</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span>
            <span class="n">noise_dataset_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
            <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16000</span><span class="p">,</span>
            <span class="n">noise_level</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">noise_dataset_dir</span><span class="p">):</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Directory doesn`t exist: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">noise_dataset_dir</span><span class="p">))</span>
            <span class="k">raise</span> <span class="ne">IOError</span>

        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Create Noise injector...&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">sample_rate</span> <span class="o">=</span> <span class="n">sample_rate</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span> <span class="o">=</span> <span class="n">noise_level</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_load_audio</span> <span class="o">=</span> <span class="n">load_audio</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">audio_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_audio_paths</span><span class="p">(</span><span class="n">noise_dataset_dir</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_noiseset</span><span class="p">(</span><span class="n">noise_dataset_dir</span><span class="p">)</span>

        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Create Noise injector complete !!&quot;</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal</span><span class="p">):</span>
        <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
        <span class="n">noise_level</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span><span class="p">)</span>

        <span class="n">signal_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
        <span class="n">noise_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">noise</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">signal_length</span> <span class="o">&gt;=</span> <span class="n">noise_length</span><span class="p">:</span>
            <span class="n">noise_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="o">*</span> <span class="p">(</span><span class="n">signal_length</span> <span class="o">-</span> <span class="n">noise_length</span><span class="p">))</span>
            <span class="n">noise_end</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">noise_start</span> <span class="o">+</span> <span class="n">noise_length</span><span class="p">)</span>
            <span class="n">signal</span><span class="p">[</span><span class="n">noise_start</span><span class="p">:</span> <span class="n">noise_end</span><span class="p">]</span> <span class="o">+=</span> <span class="n">noise</span> <span class="o">*</span> <span class="n">noise_level</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="n">signal</span> <span class="o">+=</span> <span class="n">noise</span><span class="p">[:</span><span class="n">signal_length</span><span class="p">]</span> <span class="o">*</span> <span class="n">noise_level</span>

        <span class="k">return</span> <span class="n">signal</span>

    <span class="k">def</span> <span class="nf">create_audio_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset_path</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="n">audio_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
        <span class="n">noise_audio_paths</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">dataset_path</span><span class="p">)</span>
        <span class="n">num_noise_audio_data</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">noise_audio_paths</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_noise_audio_data</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">noise_audio_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;.pcm&#39;</span><span class="p">)</span> \
                    <span class="ow">or</span> <span class="n">noise_audio_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;.wav&#39;</span><span class="p">)</span> \
                    <span class="ow">or</span> <span class="n">noise_audio_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;.flac&#39;</span><span class="p">):</span>
                <span class="n">audio_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">noise_audio_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">audio_paths</span>

    <span class="k">def</span> <span class="nf">create_noiseset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset_path</span><span class="p">):</span>
        <span class="n">dataset</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">audio_path</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">audio_paths</span><span class="p">:</span>
            <span class="n">audio_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_path</span><span class="p">,</span> <span class="n">audio_path</span><span class="p">)</span>
            <span class="n">noise</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_audio</span><span class="p">(</span><span class="n">audio_path</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">del_silence</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

            <span class="k">if</span> <span class="n">noise</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">dataset</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">noise</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">dataset</span></div>


<div class="viewcode-block" id="TimeStretchAugment"><a class="viewcode-back" href="../../../../modules/Data Augment.html#openspeech.data.audio.augment.TimeStretchAugment">[docs]</a><span class="k">class</span> <span class="nc">TimeStretchAugment</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Time-stretch an audio series by a fixed rate.</span>

<span class="sd">    Inputs:</span>
<span class="sd">        signal: np.ndarray [shape=(n,)] audio time series</span>

<span class="sd">    Returns:</span>
<span class="sd">        y_stretch: np.ndarray [shape=(round(n/rate),)] audio time series stretched by the specified rate</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">min_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span> <span class="n">max_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.4</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TimeStretchAugment</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">min_rate</span> <span class="o">=</span> <span class="n">min_rate</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_rate</span> <span class="o">=</span> <span class="n">max_rate</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">librosa</span><span class="o">.</span><span class="n">effects</span><span class="o">.</span><span class="n">time_stretch</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_rate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_rate</span><span class="p">))</span></div>


<div class="viewcode-block" id="JoiningAugment"><a class="viewcode-back" href="../../../../modules/Data Augment.html#openspeech.data.audio.augment.JoiningAugment">[docs]</a><span class="k">class</span> <span class="nc">JoiningAugment</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Data augment by concatenating audio signals</span>

<span class="sd">    Inputs:</span>
<span class="sd">        signal: np.ndarray [shape=(n,)] audio time series</span>

<span class="sd">    Returns: signal</span>
<span class="sd">        - **signal**: concatenated signal</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">JoiningAugment</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signals</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">signal</span> <span class="k">for</span> <span class="n">signal</span> <span class="ow">in</span> <span class="n">signals</span><span class="p">])</span></div>
</pre></div>

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